code: def __init__(
      self,
      random_state: Optional[Union[int, np.random.RandomState]] = None,
  ) -> None:
      # Use custom implementation because sklearn implementation cannot
      # handle float32 input matrix
      from sklearn.preprocessing import Normalizer
  
      self.preprocessor = Normalizer(copy=False)
  
file_path: auto-sklearn/autosklearn/pipeline/components/data_preprocessing/rescaling/normalize.py
imports: from typing import Dict, Optional, Tuple, Union
  import numpy
  from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE
  from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm
  from autosklearn.pipeline.components.data_preprocessing.rescaling.abstract_rescaling import Rescaling
  from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA
label_desc: Use image node_id 1 to create a new NormalizerComponent object from inherited base classes: Rescaling, AutoSklearnPreprocessingAlgorithm with example: obj = NormalizerComponent(random_state)